「red-team」の検索結果

Test application security against OWASP Top 10 (2025) with automated CI tooling: OWASP ZAP (DAST), dependency/supply-chain scanning (OSV-Scanner, SBOM, provenance), Semgrep SAST, auth/session tests (JWT, OAuth, RBAC), and XSS/CSRF/SQLi/SSRF Playwright patterns. Use when: "security test," "OWASP," "vulnerability," "ZAP," "XSS," "SSRF," "dependency scan," "auth testing," "OWASP LLM Top 10." Scope is automated scanning + negative-path security tests in CI, not manual penetration testing. Not for: mapping security controls to regulations (SOC 2, HIPAA, PCI, GDPR) — use compliance-testing; pipeline stage wiring and deploy gating mechanics — use ci-cd-integration; purely functional API auth/input tests with no attacker model — see api-testing; testing your product's own LLM features or defending the agent itself (prompt-injection detector, indirect injection, jailbreak red-teaming) — use ai-system-testing. Related: ci-cd-integration, compliance-testing, api-testing, shift-left-testing, ai-system-testing.

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红队渗透 / 攻防 — 受授权的红队作业者 + 渗透测试工程师 + 攻击型安全顾问的认知操作系统 (侦察 OSINT / 外网渗透 / 内网 AD 渗透 BloodHound + Kerberoasting + ADCS 利用 + 横向移动 / Web 应用渗透 OWASP WSTG / 移动 OWASP MASTG / 云渗透 AWS Azure GCP IAM 路径 + 容器逃逸 + K8s / C2 操作 Cobalt Strike Sliver Mythic Havoc + OPSEC / 初始访问 + AV EDR 绕过 (仅授权场景) / 无线 RF / 物理社工 / 报告与整改 / 框架 MITRE ATT&CK + D3FEND + PTES + OSSTMM + NIST 800-115 + Kill Chain / 法律伦理 CFAA + 网络安全法 + 刑法 285 286 + 数据安全法 + GDPR + 授权书 + 范围 + 交战规则 — 不含 黑产 / 未授权攻击 / 大规模 exploitation / 供应链投毒 / 未授权 DoS — 这是 重罪 + 行业封杀 + 律师吊销, 本 skill 严守 authorized-only 边界 — 也不含 蓝队 SOC + 恶意软件 即服务 / 僵尸网络 / 勒索软件作者 — 这是 cybercrime 不是 红队) (Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasion (phishing infrastructure, payload development, AV / EDR evasion, BYOVD, AMSI / ETW bypass — strictly for authorized engagements), (i) wireless / RF (WPA2/3, evil twin, Wi-Fi pivots), (j) physical / social engineering (badge cloning, pretexting, vishing — under engagement letter), (k) reporting & remediation (executive summary, technical findings, CVSS, MITRE ATT&CK mapping, retest), (l) frameworks & methodology (MITRE ATT&CK, MITRE D3FEND, PTES, OSSTMM, NIST SP 800-115, OWASP WSTG / MASTG, Cyber Kill Chain, Unified Kill Chain, Diamond Model), (m) law & ethics (CFAA US, Computer Misuse Act UK, 中国 刑法 285/286 + 网络安全法 + 数据安全法, GDPR for tested EU systems, engagement letter, scope, rules of engagement, safe harbor for bug bounty); NOT criminal hacking / 黑产 / unauthorized targeting / mass exploitation / supply-chain compromise / DoS against unconsented systems (这是 重罪 + 业内开除 + 律师执照吊销, 本 skill 严守 authorized-only 边界), NOT pure defensive blue team / SOC analyst tradecraft (是 平行学科, 仅做 边界标注 + ATT&CK 反推方向), NOT malware-as-a-service development / botnet ops / ransomware authoring (是 cybercrime 不是 红队), NOT 'ethical hacking' 在 'just curious 看看' 自我合理化的灰色操作 (违反 authorization 原则即不是 红队).) Master OS — automated mastery of Cybersecurity Red Team / Offensive Security Operations — the cognitive operating system of authorized red team operators, penetration testers, and offensive security consultants covering (a) reconnaissance & OSINT (passive + active discovery, asset surface mapping), (b) external network pentest (perimeter, exposed services, web), (c) internal network / Active Directory pentest (AD enumeration via BloodHound, Kerberos abuse — Kerberoasting / AS-REP-roasting / Unconstrained delegation / S4U2self, NTLM relay, ADCS abuse, GPO abuse, lateral movement, privilege escalation), (d) web application pentest (OWASP WSTG, authentication, authorization, SSRF, XXE, deserialization, SSTI, prototype pollution, GraphQL, JWT, API), (e) mobile pentest (OWASP MASTG, iOS / Android, instrumentation Frida / Objection, MASVS), (f) cloud pentest (AWS / Azure / GCP — IAM enumeration, privilege escalation paths, container escape, K8s RBAC, serverless), (g) C2 operations & post-exploitation (Cobalt Strike / Sliver / Mythic / Havoc, beacon ops, malleable profiles, OPSEC), (h) initial access & evasi

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Write, run, and analyze structured test suites for Agentforce agents — functional AND security. TRIGGER when: user writes or modifies test spec YAML (AiEvaluationDefinition); runs sf agent test create, run, run-eval, or results commands; asks about test coverage strategy, metric selection, or custom evaluations; interprets test results or diagnoses test failures; asks about batch testing, regression suites, or CI/CD test integration; requests security testing, OWASP LLM Top 10, red-teaming, penetration testing, prompt-injection tests, a security grade, or a vulnerability assessment of an agent. DO NOT TRIGGER when: user creates, modifies, previews, or debugs .agent files (use agentforce-generate); deploys or publishes agents; writes Agent Script code; uses sf agent preview for development iteration; analyzes production session traces (use agentforce-observe); performs a static safety review of .agent file content (use agentforce-generate Section 15).

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Builds QA harnesses and agent evals. Use when evaluating tool, trace, red-team, regression, multi-agent, or carried-workspace coding trajectories. Not for metric design (ai-evals).

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Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to "audit our AI guardrails", "can our filters be bypassed", or "check guardrail false positives".

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AI-driven dynamic security fuzzing, exploit generation (XSS, SQLi, SSRF, Prompt Injection), and automated patch remediation / Fuzzing keamanan dinamis berbasis AI, eksploitasi, dan remediasi otomatis.

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Multi-provider deliberation panel for COMPLEX, multi-view questions — architecture, strategy, market research, and codebase sweeps. Runs a 4-stage deliberation across the provider fleet (diverge → contrarian red-team → adversarial verify → cited synthesis), the same multi-perspective rigour the plan-gate already applies to plan reviews, generalised to arbitrary questions. Use when one model's answer isn't enough and you want convergence THROUGH disagreement + verification, not a single opinion.

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Skeptical analysis and critique of claims, plans, and code without taking anyone at face value. Use when the user says "don't trust me", wants adversarial review, red-teaming, assumption checking, or verification that generated or proposed code is actually correct. Triggers on skepticism, critique everything, verify claims, challenge assumptions, red team, devil's advocate, prove it, smell test.

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Simulate rigorous, fair peer review on a paper — in two modes. (A) Red-team YOUR OWN draft before submission to predict the reviews you'll get and get a fix list. (B) Review SOMEONE ELSE'S paper when you're an assigned reviewer or helping your advisor — produce a fair, venue-formatted, submission-ready review. Use whenever the user gives a paper (PDF, .tex, .md, arXiv link, or pasted text) and wants to predict reviews, find weaknesses before reviewers do, stress-test a submission, OR write a real review of another paper. Grounded in official NeurIPS/ICLR/ACL reviewer guidelines; every criticism pinned to a location, and checked against ACL's H1–H17 list of illegitimate critiques. 两种模式:审自己的稿(投稿前红队)/ 审别人的稿(当审稿人或帮导师),按会议官方标准评,绝不脑补、绝不发不正当批评。

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Use when challenging a prevailing analytical judgment, the user asks "what is the opposing case?" / "argue the other side" / "play devil's advocate", or wants an assessment stress-tested before release. Takes the team's own lead judgment and builds the strongest case that it is wrong. A contrarian technique. It does not model what an adversary would do next, which is Red Team Analysis. Formerly /red-team-analysis.

日本語の概要は準備中です。原文の説明を表示しています。

Red team tactics principles based on MITRE ATT&CK. Attack phases, detection evasion, reporting.

日本語の概要は準備中です。原文の説明を表示しています。

Execute commands and binaries on remote Windows systems utilizing PsExec and SMB/RPC mechanisms. This skill details the mechanics behind tools like Sysinternals PsExec, Impacket's psexec.py, and their role in lateral movement via hidden administrative shares.

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Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.

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Test Retrieval-Augmented Generation (RAG) systems for data poisoning, prompt injection via retrieved documents, and data exfiltration through manipulated context windows. Use this skill when assessing RAG-based chatbots, knowledge bases, enterprise AI assistants, or any system that augments LLM responses with external document retrieval. Covers document injection, embedding manipulation, knowledge base poisoning, and cross-document inference attacks.

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Execute advanced evasion by injecting malicious code into the memory space of a legitimate, suspended process (Process Hollowing). This skill details techniques to bypass static and dynamic analysis by masking malicious activity behind trusted processes like svchost.exe or explorer.exe.

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Execute lateral movement within Active Directory environments using credential material (NTLM hashes and Kerberos tickets) instead of plaintext passwords. Use this skill when engaging in red team operations or internal network penetration tests to expand access, bypass authentication portals, and achieve Domain Dominance without relying on crackable passwords.

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Plan and execute authorized phishing and social engineering campaigns for red team engagements. Use this skill when conducting simulated social engineering attacks including email phishing, spear phishing, vishing, pretexting, and credential harvesting. Covers GoPhish setup, pretext development, payload delivery, and measuring human vulnerability. Requires explicit written authorization before any testing.

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Generate sophisticated initial access payloads designed to bypass email gateways and endpoint antivirus systems while executing Command and Control (C2) agents. Use this skill to craft malicious LNKs, ISOs, Weaponized Office Documents, and HTML Smuggling techniques for Red Team campaigns.

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Extract sensitive training data and internal capabilities from AI models through repeated programmatic querying. Use this skill when testing LLMs and ML models for data privacy leaks, intellectual property exposure, or unintended memorization. Covers membership inference, prompt extraction, training data reconstruction, and API boundary testing.

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Test Model Context Protocol (MCP) servers and tool-calling systems for security vulnerabilities including tool injection, parameter manipulation, privilege escalation, and data exfiltration through AI agent tool interfaces. Use this skill when assessing MCP server implementations, AI agent tool integrations, or any system that exposes tools to language models. Covers tool confusion attacks, cross-tool exploitation, and MCP server hardening assessment.

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Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

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Exploit AI applications using Indirect Prompt Injection. This skill focuses on hiding malicious instructions within data sources (web pages, documents, emails) that the LLM processes, causing the AI to execute unintended actions or leak data without direct user interaction.

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Exploit an application's absolute trust in its underlying LLM (Overreliance). Use this skill to induce critical "hallucinations" (confident falsehoods) that cause downstream logical systems or automated agents tracking the LLM's output to make destructive actions or grant unauthorized access.

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Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI models.

日本語の概要は準備中です。原文の説明を表示しています。